Video surveillance systems are often deployed in schools, government buildings, small businesses, retail stores and corporate offices, and even many residences. These surveillance systems are typically comprised of surveillance cameras that capture image data, image data storage systems that store the image data along with possibly metadata, and increasingly analytics systems that analyze the image data and possibly generate the metadata.
The installation of surveillance systems is often complex and time consuming. First, an installer has to identify and select locations throughout the building, for example, to install the surveillance cameras. Next, the installer has to physically mount the surveillance cameras in the building and supply them with power. Data connections between the surveillance cameras and the image data storage systems and possibly the analytics systems must then be established. In many cases, this requires running data cables from data transmission devices (e.g., routers, switches, and hubs) to all of the devices, although wireless systems are becoming increasingly common.
After the physical installation, the installer must then configure the systems. Generally, configuration of the surveillance camera systems is tedious, requiring the installer to repeatedly enter configuration information such as device names, Internet Protocol (IP) addresses, media access control (MAC) addresses, device locations, and/or port settings for devices. In many cases, the installer has to travel between different locations throughout the building to configure the various components, the network, and any monitoring station.
Recently, the surveillance camera systems have begun using open standards. Among other advantages, this enables users to more easily access the image data from the surveillance cameras. On user devices such as computer workstations and mobile computing devices such as tablets, smart phones and laptop computers, users can access and select image data from specific surveillance cameras for real-time viewing upon and downloading to the user devices. In addition, the users on the user devices can also access previously recorded image data stored on the image data storage systems.
Another trend concerns the analytics systems, which are becoming increasingly powerful. Often, the analytics systems will track moving objects against fixed background models. More sophisticated functions include object detection to determine the presence of an object or classify the type of object or event. The analytics systems generate video primitives or metadata for the detected objects and events, which the analytics systems can further process or send over the data networks to other systems for storage and incorporation into the image data as metadata, for example.
While analytics systems have historically been separate systems apart from the surveillance cameras, the surveillance cameras themselves are increasingly providing this functionality. Integrating the analytics functionality within the cameras themselves has advantages. It eliminates the cost and maintenance associated with deploying a separate analytics system to accomplish the same objective, and enables more efficient analysis by eliminating the messaging overhead associated with sending the image data over the data network for analysis by the separate analytics systems, in examples.
Similar trends have emerged in the case of image data storage systems. Surveillance cameras are being offered that include image data storage on the camera itself. Such surveillance cameras are especially attractive to smaller organizations such as stores, small companies, and local offices that want to reduce installation and maintenance expenses. Each camera can function as a stand-alone unit, and as a result there is no need to have a specialized image data storage system. With the advent of improved image compression on one hand, and the decreasing costs of data storage on the other hand, each surveillance camera is often able to store substantially larger amounts of image data than it generates.
At the same time, remote cloud image data storage systems are also available. These systems can offer a number of advantages over local image data storage systems. The organizations have fewer components to buy and install, which lowers both purchase and depreciation cost. Organizations can also pay on a per usage basis for infrequently used value-added services. Finally, the service providers of the cloud storage systems bear the responsibility of maintaining and upgrading the storage systems and their capabilities, the cost of which the service providers can share across their managed clients.